混淆矩阵错误“分类指标无法处理多标签指标和多类目标的混合" [英] confusion matrix error "Classification metrics can't handle a mix of multilabel-indicator and multiclass targets"

问题描述

我得到一个

Classification metrics can't handle a mix of multilabel-indicator and multiclass targets

当我尝试使用混淆矩阵时出错.

error when I try to use confusion matrix.

我正在做我的第一个深度学习项目.我是新手.我正在使用由 keras 提供的 mnist 数据集.我已经成功地训练和测试了我的模型.

I am doing my first deep learning project. I am new to it. I am using the mnist dataset provided by keras. I have trained and tested my model successfully.

然而，当我尝试使用 scikit learn 混淆矩阵时，我得到了上述错误.我已经搜索了一个答案，虽然有关于这个错误的答案，但没有一个对我有用.从我在网上找到的内容来看，它可能与损失函数有关(我在代码中使用了 categorical_crossentropy).我尝试将其更改为 sparse_categorical_crossentropy 但这只是给了我

However, when I try to use the scikit learn confusion matrix I get the error stated above. I have searched for an answer and while there are answers on this error, none of them worked for me. From what I found online it probably has something to do with the loss function (I use the categorical_crossentropy in my code). I tried changing it to sparse_categorical_crossentropy but that just gave me the

Error when checking target: expected dense_2 to have shape (1,) but got array with shape (10,)

当我在模型上运行 fit() 函数时.

when I run the fit() function on the model.

这是代码.(为了简洁起见，我省略了进口)

This is the code. (I have left out the imports for the sake of brevity)

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(28 * 28,)))
model.add(Dense(10, activation='softmax')) 

model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.astype('float32') / 255

test_images = test_images.reshape((10000, 28 * 28))
test_images = test_images.astype('float32') / 255

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

model.fit(train_images, train_labels, epochs=10, batch_size=128)

rounded_predictions = model.predict_classes(test_images, batch_size=128, verbose=0)

cm = confusion_matrix(test_labels, rounded_predictions)

我该如何解决这个问题?

How can i fix this?

推荐答案

混淆矩阵需要标签和标签预测作为个位数，而不是作为 one-hot 编码的向量；尽管您已经使用 model.predict_classes() 进行了预测，即

Confusion matrix needs both labels & predictions as single-digits, not as one-hot encoded vectors; although you have done this with your predictions using model.predict_classes(), i.e.

rounded_predictions = model.predict_classes(test_images, batch_size=128, verbose=0)
rounded_predictions[1]
# 2

你的 test_labels 仍然是 one-hot 编码:

your test_labels are still one-hot encoded:

test_labels[1]
# array([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)

因此，您也应该将它们转换为个位数，如下所示:

So, you should convert them too to single-digit ones, as follows:

import numpy as np
rounded_labels=np.argmax(test_labels, axis=1)
rounded_labels[1]
# 2

之后，混淆矩阵应该就可以了:

After which, the confusion matrix should come up OK:

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(rounded_labels, rounded_predictions)
cm
# result:
array([[ 971,    0,    0,    2,    1,    0,    2,    1,    3,    0],
       [   0, 1121,    2,    1,    0,    1,    3,    0,    7,    0],
       [   5,    4,  990,    7,    5,    3,    2,    7,    9,    0],
       [   0,    0,    0,  992,    0,    2,    0,    7,    7,    2],
       [   2,    0,    2,    0,  956,    0,    3,    3,    2,   14],
       [   3,    0,    0,   10,    1,  872,    3,    0,    1,    2],
       [   5,    3,    1,    1,    9,   10,  926,    0,    3,    0],
       [   0,    7,   10,    1,    0,    2,    0,  997,    1,   10],
       [   5,    0,    3,    7,    5,    7,    3,    4,  937,    3],
       [   5,    5,    0,    9,   10,    3,    0,    8,    3,  966]])

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